A Robust Memristor Based on Epitaxial Vertically Aligned Nanostructured BaTiO3-CeO2 Films on Silicon

With the exploration of ferroelectric materials, researchers have a strong desire to explore the next generation of non-volatile ferroelectric memory with silicon-based epitaxy, high-density storage, and algebraic operations. Herein, a silicon-based memristor with an epitaxial vertically aligned nanostructures BaTiO3-CeO2 film based on La0.67Sr0.33MnO3/SrTiO3/Si substrate is reported. The ferroelectric polarization reversal is optimized through the continuous exploring of growth temperature, and the epitaxial structure is obtained, thus it improves the resistance characteristic, the multi-value storage function of five states is achieved, and the robust endurance characteristic can reach 10(9) cycles. In the synapse plasticity modulated by pulse voltage process, the function of the spiking-time-dependent plasticity and paired-pulse facilitation is simulated successfully. More importantly, the algebraic operations of addition, subtraction, multiplication, and division are realized by using fast speed pulse of the width approximate to 50 ns. Subsequently, a convolutional neural network is constructed for identifying the CIFAR-10 dataset, to simulate the performance of the device; the online and offline learning recognition rate reach 90.03% and 92.55%, respectively. Overall, this study paves the way for memristors with silicon-based epitaxial ferroelectric films to realize multi-value storage, algebraic operations, and neural computing chip applications.

Automated summary

This study presents a silicon-based memristor with an epitaxial ferroelectric film, which enables multi-value storage, algebraic operations, and neural computing chip applications. The film exhibits optimized ferroelectric polarization reversal and epitaxial structure, leading to improved resistance characteristics and robust endurance. Experimental results demonstrate successful simulation of synaptic plasticity and algebraic operations by controlling pulse voltage. The memristor achieves high recognition rates in identifying the CIFAR-10 dataset.